Maximizing Energy Efficiency of UAV-Assisted RF-Powered Networks with Quality-of-Service Constraints

In this paper, we investigate a UAV-assisted wireless powered communication network (WPCN) where UAVs act as access points (APs) to periodically serve a group of ground sensor nodes (SNs). Unlike fixed APs in traditional WPCNs, UAV-assisted WPCNs can leverage UAV mobility to maximize system throughput by optimizing the UAV trajectory and wireless resource allocation. However, due to the limited data buffer capacity of the SNs, UAVs may fail to provide timely services, leading to data overflow. Therefore, UAVs must offer efficient and timely services to the SNs. Our objective was to maximize the total energy efficiency of all ground SNs by jointly optimizing UAV transmit power, downlink (DL) wireless energy transfer (WET) time, uplink (UL) wireless information transfer (WIT) time, and SN transmit power under minimal quality-of-service (QoS) constraints. However, the formulated optimization problem is non-convex and difficult to solve directly. To address this, we applied fractional programming theory to transform the non-convex problem into a tractable form. Subsequently, a block coordinate descent-based algorithm was proposed to obtain a near-optimal resource allocation scheme. Extensive simulation results show that our proposed method achieved significantly better performance in terms of system throughput and energy efficiency compared to other benchmark solutions.